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| Cross-Social Media Platform Emergency Knowledge Collaboration Based on Multimodal Heterogeneous Information Networks |
| Zhou Wei1,2, An Lu1,2,3, Wu Xuan4, Han Ruilian1,2, Li Gang1,2 |
1.Center for Studies of Information Resources, Wuhan University, Wuhan 430072
2.School of Information Management, Wuhan University, Wuhan 430072
3.Institute of Data Intelligence, Wuhan University, Wuhan 430072
4.Department of Information Management, Peking University, Beijing 100871 |
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Abstract Effective collaboration of multimodal emergency knowledge across social media platforms is critical for improving information integration and intelligent response during public emergencies. To address the limitations in network modeling and representation accuracy and the lack of collaboration pathways in current multimodal heterogeneous information networks, this study proposes a cross-platform emergency knowledge collaboration method. First, multimodal data (text, images, and videos) from multiple platforms were collected to construct heterogeneous information networks with a unified structure and semantic alignment. Second, an enhanced heterogeneous graph convolutional network (HGCN) model is proposed and combined with a multi-head attention fusion mechanism to improve the semantic representation. Finally, node- and group-level collaboration strategies are introduced to support structural linkage and semantic coordination across platforms. In experimental results, the proposed Enhanced-HGCN achieved the best clustering performance, with a normalized mutual information (NMI) score of 0.5660, significantly outperforming the baseline models. The cross-platform network achieved a clustering coefficient of 0.451 and reduced the betweenness centrality to 0.83×10-4, confirming the overall effectiveness of the proposed method for semantic fusion and structural optimization.
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Received: 12 May 2025
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